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Abstract

Introduction

Previous studies have shown that traditional risk factors such as hypercholesterolemia
and hypertension account for only a small proportion of the dramatically increased
risk of atherosclerotic coronary artery disease (CAD) in systemic lupus erythematosus
(SLE). However, in these studies, exposure to risk factors was measured only at baseline.
In this study, our objective was to compare measures of cumulative exposure with remote
and recent values for each of total cholesterol (TC), systolic (SBP), and diastolic
(DBP) blood pressure in terms of ability to quantify risk of atherosclerotic CAD in
patients with SLE.

Methods

Patients in the Toronto lupus cohort had TC and BP measured at each clinic visit and
were followed up prospectively for the occurrence of CAD. For each patient, arithmetic
mean, time-adjusted mean (AM) and area-under-the-curve (AUC) were calculated for serial
TC, SBP, and DBP measurements. Proportional hazards regression models were used to
compare these summary measures with recent and first-available ("remote") measurements
in terms of ability to quantify risk of CAD events, defined as myocardial infarction,
angina, or sudden cardiac death.

Results

The 991 patients had a mean ± SD of 19 ± 19 TC measurements per patient. Over a follow-up
of 6.7 ± 6.4 years, 86 CAD events occurred; although remote TC was not significantly
predictive of CAD, mean and AM TC were more strongly predictive (hazard ratio (HR)
2.07; P = 0.003) than recent TC (HR 1.86, P = 0.001). AUC TC was not predictive of CAD. A similar pattern was seen for DBP and
SBP. Older age, male sex, higher baseline and recent disease activity score, and corticosteroid
use also increased CAD risk, whereas antimalarials were protective.

Conclusions

In contrast to the population-based Framingham model, first-available TC and BP are
not predictive of CAD among patients with SLE, in whom measures reflecting cumulative
exposure over time are better able to quantify CAD risk. This is an important consideration
in future studies of dynamic risk factors for CAD in a chronic relapsing-remitting
disease such as SLE. Our findings also underpin the importance of adequate control
of SLE disease activity while minimizing corticosteroid use, and highlight the cardioprotective
effect of antimalarials.

Introduction

Systemic lupus erythematosus (SLE) is associated with a dramatically increased risk
of atherosclerotic coronary artery disease (CAD), such that women with SLE aged 34
to 44 years are more than 50 times more likely to develop myocardial infarction (MI)
than are age-matched peers [1]. Traditional risk factors measured at baseline, as defined in the Framingham model,
do not fully account for this increased risk [2].

In the general population, it has been shown that recent and remote blood pressure
(BP) predict cardiovascular risk incrementally over current BP [3]. In an inception cohort of patients with SLE, those with sustained hypercholesterolemia
in the first 3 years of their disease were shown to be at greatest risk of cardiovascular
events over 12 to 14 years of follow-up, compared with those who had persistently
normal cholesterol or "variable" hypercholesterolemia in the first 3 years of disease
[4]. We previously showed that both TC and BP take a variable course in patients with
SLE and that almost half of the total variance over time in both TC and BP is seen
within rather than between patients [5]. These findings suggest that the risk of future coronary events might be best quantified
by using strategies that take into account multiple measurements of risk factors over
time.

In this prospective proof-of-concept cohort study, we sought to compare "summary measures"
of cumulative exposure to TC, SBP, and DBP, with single-point-in-time measurements
of these risk factors (both recent and remote), in terms of ability to quantify CAD
risk.

Materials and methods

Patients

Patients attending the University of Toronto lupus clinic are routinely seen at 2-
to 6-monthly intervals wherein clinical and laboratory data, including TC, systolic
blood pressure (SBP), and diastolic blood pressure (DBP) levels are obtained and recorded
according to a set protocol. Patients are followed up prospectively for the occurrence
of CAD events. In this study, we included patients who had two or more measurements
of TC, SBP, and DBP taken before a CAD event (or last visit), in whom the gap between
measurements did not exceed 18 months. Patients with a history of CAD before the start
of the study were excluded. All patients fulfilled four or more of the American College
of Rheumatology classification criteria for SLE, or had three criteria and a typical
lesion of SLE on skin or renal biopsy [6,7]. Informed consent was obtained from all participants, and the study was approved
by the research ethics board of the University Health Network.

Methods

Measurement of TC, SBP, and DBP

Each measurement of TC, SBP, and DBP was tied to a clinic visit. TC was measured in
plasma by using a commercial assay (Boehringer Mannheim kit 236691, Indianapolis,
IN, USA) and recorded in millimoles per liter. As only small, clinically insignificant
differences in TC are found when measured in the fasting or nonfasting state, nonfasting
samples were used [8]. SBPs and DBPs were measured in millimeters of mercury (mm Hg) at every visit by
using a manual sphygmomanometer. The patient was allowed to rest for 5 minutes in
the sitting position. The reading was taken on the right arm, supported at the level
of the heart. Korotkoff phase V (disappearance) was recorded as DBP.

Calculation of "summary measures" of TC, SBP, and DBP

For each of TC, SBP, and DBP, an arithmetic mean of all available measurements in
each patient was calculated as the sum of all individual measurements divided by the
total number of measurements. In each patient, for each of TC, SBP, and DBP, a time-adjusted
mean (AM) was also calculated by using the formula:

where xi is the level of the variable at visit I, and ti is the time interval between visit i and i-1. By incorporating the time interval between
measurements in its calculation, the AM takes into account the length of time that
TC, SBP, and DBP are presumed to have remained at a particular level. The arithmetic
mean and AM were reported in millimoles per liter for TC and millimeters of mercury
for SBP and DBP. In each patient, for each of TC, SBP, and DBP, the area-under-the-curve
(AUC) was calculated by using integral calculus. AUC is reported in millimoles per
liter multiplied by t and millimeters mercury multiplied by t for each of TC and BP, respectively, where t is the unit of time, in this case, months. For any given visit (Vi), the summary measure for each of TC, SBP, and DBP was calculated from the first
study visit (V1) up to and including the visit before (Vi-1), thus ensuring that for all time intervals, exposure preceded outcome. The last
visit (VL) was either a visit at which a CAD event was recorded or the last clinic visit as
of August 2008 in those who remained CAD free.

Covariates

Covariates included in the proportional hazards models were sex, age, disease duration,
disease-activity score (Systemic Lupus Erythematosus Disease Activity Index 2000;
SLEDAI-2K), anti-phospholipid antibodies, "other" classic cardiovascular risk factors
(diabetes and smoking), medications including corticosteroids, antimalarials, immunosuppressives,
antihypertensives, and lipid-lowering therapy (statins), all recorded at baseline,
and at each and every visit. Age and disease duration (from diagnosis to Vi) were reported in years. SLEDAI-2K is an organ-weighted index of disease activity,
scored from 0 to 105, with higher scores indicating more-active disease [9]. Antimalarials included chloroquine and hydroxychloroquine. Immunosuppressives included
methotrexate, azathioprine, mycophenolate mofetil, cyclosporine, and cyclophosphamide.
Antihypertensives included diuretics, β-blockers, calcium channel blockers, angiotensin-converting
enzyme inhibitors, and angiotensin type II receptor blockers. The cumulative corticosteroid
dose over the period of follow-up from study entry (V1) to the last visit (VL) was calculated and reported in grams. Use of all other medications was reported
categorically at each visit, irrespective of dose. Diabetes was defined as fasting
plasma glucose > 7.0 mmol/L or diabetes therapy ever. Current smoking was defined
as smoking an average of one or more cigarette/s per day in the past month.

Outcome variables

CAD events were angina pectoris, myocardial infarction (MI), and sudden cardiac death.
MI was defined as one of definite electrocardiographic (ECG) abnormalities, or typical
symptoms with probable ECG abnormalities and abnormal enzymes (≥2 times upper limit
of normal), or typical symptoms and abnormal enzymes. Angina pectoris was defined
as severe pain or discomfort over the upper or lower sternum or anterior left chest
and left arm, of short duration, relieved by rest or vasodilators in the absence of
active SLE, or in the presence of atherosclerotic vascular disease elsewhere (for
example, atherosclerotic peripheral vascular or cerebrovascular disease). The diagnosis
of angina and MI required confirmation by a cardiologist. Sudden cardiac death was
defined as death with undetermined cause but presumed cardiac.

A CAD event that occurred between Vi and Vi+1 was recorded at Vi+1. For patients who had more than one CAD event, only the first was used in analysis.
Some patients may have had both angina and MI recorded for the first time at a particular
visit; this was treated as only one event rather than two.

Univariate comparisons

For each of the TC and BP models, univariate comparisons of demographic, disease-
and treatment-related variables and traditional cardiac risk factors in patients who
had CAD events and those that remained CAD free were performed by using t tests for continuous variables and χ2 tests for categoric variables. In case of non-normally distributed data, Mann-Whitney
U tests were used for continuous variables. Two-sided P values (P) ≤ 0.05 were considered to be significant.

Time-constant regression models

For each of TC, SBP, and DBP, two time-constant proportional hazards models were run.
Variables in the first model included first available (baseline) measurement of TC
(or SBP, DBP), along with sex, age, SLEDAI-2K score at study entry (V1 or "baseline"), and anti-phospholipid antibodies, diabetes, smoking, corticosteroid,
antimalarial, immunosuppressive, antihypertensive, and lipid-lowering medication use
"ever" from V1 to VL-1.

The second model included the average (arithmetic mean) of the first two available
measurements of TC (or SBP or DBP) and all covariates included in the first Cox model.

Time-dependent regression models

For each of TC, SBP, and DBP, we also ran four time-dependent proportional hazards
regression models. In the first model, recent measurements of TC (or SBP or DBP) were
used in a dynamic manner, varying from visit to visit. In the remaining three models,
summary measures (mean, AM, AUC) were used in a time-dependent manner (that is, updated
from visit to visit). Covariates in these models included sex, age, SLEDAI-2K score,
anti-phospholipid antibodies, diabetes, smoking, corticosteroid, antimalarial, immunosuppressive,
anti-hypertensive and lipid-lowering medication use, also treated in a time-dependent
fashion (that is, updated from visit to visit). For each of TC, SBP, and DBP at each
visit, single point, mean, and AM measurements were strongly correlated. Therefore,
each summary measure was analyzed in a separate model.

Both time-constant and time-dependent models are reported as hazard ratios (HRs) with
accompanying 95% confidence interval (95% CI) and P value, for each of the predictor variables and covariates. All statistical analyses
were performed by using the SAS software version 9.1 (SAS Institute, Inc., Cary, NC,
U.S.A.).

Results

Characteristics of the patients in this study are presented in Table 1. Overall, the BP dataset contained 991 patients, whereas the TC dataset comprised
956 patients. In each dataset, patients were mostly female (88%) and mostly Caucasian
(70%). A total of 94 coronary events occurred (75 angina, 25 MI, and two sudden cardiac
deaths; eight had both angina and MI) in the BP dataset, whereas the TC dataset contained
86 coronary events (71 angina, 20 MI, and two sudden cardiac deaths; seven had both
angina and MI). The mean ± SD age and disease duration at entry into the study were
very similar for both datasets (37.1 ± 14.0 and 6.1 ± 7.9 years, respectively, for
the BP dataset). Likewise, mean ± SD SLEDAI-2K score and Systemic Lupus Erythematosus
International Collaborating Clinics/American College of Rheumatology Damage Index
(SLICC/ACR-DI) at study entry were similar in the two datasets (9.2 ± 7.5 and 0.5
± 1.2, respectively, for the BP dataset), indicating moderate disease activity and
minimal disease-related damage [10]. For each dataset, at entry into the study, more than 60% of patients were taking
corticosteroids, whereas approximately 40% were taking antimalarials, and 25% were
taking immunosuppressives. In each dataset, at the start of the study, approximately
22% of patients were hypertensive, 40% had hypercholesterolemia, 3% had diabetes,
and 19% were smokers. At study start, in each dataset, 25% were taking antihypertensives,
and 5% were taking lipid-lowering medications.

Summary measures for BP

The calculation of summary measures was based on 19, 579 individual measurements of
SBP and DBP, with a mean ± SD of 20 ± 20 (median, 13) serial measurements per patient.
The mean ± SD (median) time interval between measurements was 4.2 ± 2.3 (3.4) months.
The mean ± SD (median) time from study start to the visit before a CAD event (or last
clinic visit) was 6.5 ± 6.7 (4.2) years. The mean ± SD (median) length of follow-up
from study start to CAD event (or last clinic visit) was 7.0 ± 6.7 (4.6) years. Among
all patients, the mean SBP at the start of study was 123.9 ± 19.4 mm Hg, whereas the
mean DBP at the start of study was 77.6 ± 12.3 mm Hg.

Summary measures for TC

The calculation of summary measures was based on 17, 936 individual measurements of
TC, with a mean ± SD of 19 ± 19 (median, 12) serial measurements per patient. The
mean ± SD (median) time interval between TC measurements was 4.3 ± 2.3 (3.6) months.
The mean ± SD (median) time from study start to the visit before a CAD event (or last
clinic visit) was 6.3 ± 6.4 (4.2) years. The mean ± SD (median) length of follow-up
from study start to CAD event (or last clinic visit) was 6.7 ± 6.4 (4.6) years. Among
all patients, the mean TC level at the start of study was 5.3 ± 1.6 mmol/L.

In both the BP and TC datasets, patients with CAD events were more likely to have
musculoskeletal, cutaneous, renal, and nervous system manifestations of lupus and
were also more likely to have vasculitis, serositis, and fever during the course of
their disease. However, no difference was found in the prevalence of chronic renal
insufficiency, based on SLICC/ACR DI definition [10], among those with and without CAD (5.3% versus 7.1%; P = 0.51 in the BP dataset).

Proportional hazards multiple regression models

TC models

Table 3 shows the results of the proportional hazards models for CAD events by using various
measures of TC. In the time-constant models (columns 1 and 2), neither first-available
("remote") TC nor the average of first two TC levels was significantly associated
with a CAD event. However, in these models, male sex (HR = 2.02; P = 0.02), age (HR = 1.06; P < 0.0001), and SLEDAI-2K (HR = 1.03; P = 0.04) at study start (baseline), and steroid use ever (HR = 4.17; P = 0.003) were significantly associated with a CAD event. Antimalarial use ever was
protective against CAD (HR = 0.50; P = 0.003).

Table 3. Proportional hazards models for coronary outcomes using various measures of cholesterol

Table 4. Proportional hazards models for coronary outcomes using various measures of cholesterol,
including only significant covariates in the models

SBP models

Results of the proportional hazards models for CAD outcomes by using various measures
of systolic blood pressure (SBP) are presented in Table 5. In the time-constant models (columns 1 and 2), neither first available (remote)
SBP nor the average of first two SBPs was associated with a CAD event. However, in
these models, male sex (HR = 2.01; P = 0.02 for first-available SBP model; HR = 2.04; P = 0.02 for average-of-first-two SBP model), age (HR = 1.05; P < 0.0001) and SLEDAI-2K (HR = 1.03; P = 0.01) at baseline, and steroid use ever (HR = 2.73 for first available and 2.74
for average of first two SBPs, P = 0.03) were significantly associated with a CAD event. Antimalarial use ever was
protective against CAD (HR = 0.59; P = 0.02). Disease duration, elevated TC, and immunosuppressive use at baseline were
not significantly associated with CAD.

In each of the multiple regression analyses presented in Tables 3 through 6, antiphospholipid antibodies, diabetes, and smoking were consistently statistically
insignificant and therefore removed from the final models to maximize statistical
power. Data on antihypertensive use and lipid-lowering therapy were incomplete for
a proportion of visits. In subgroup analysis of these smaller datasets, neither antihypertensives
nor lipid-lowering medications were significantly associated with CAD events (data
not shown).

Discussion

Through the use of proportional hazards regression modeling in a large sample of more
than 950 patients, in whom collectively more than 18, 000 serial measurements of TC
and BP were taken over a mean duration of 6.3 years, we were able to demonstrate and
quantify the association between several important risk factors and CAD events in
SLE.

Foremost, this study highlights the important role of traditional risk factors such
as elevated TC and BP in SLE-related CAD and demonstrates a continuum of risk associated
with these variables across the range of possible values they may assume. Previous
studies have shown that traditional risk factors, such as hypercholesterolemia and
hypertension, account for only a small proportion of the increased risk of CAD in
SLE. However, in these studies, TC and BP were measured at baseline, in keeping with
the premise of the Framingham model. Here we have shown that this remote measure of
exposure to TC, SBP, or DBP is not predictive of CAD outcome among patients with SLE.
Furthermore, recent measurements of TC and BP, which are also taken at a single point
in time, are not able to quantify CAD risk to the same degree as "summary measures,
" which capture cumulative exposure to these risk factors over the course of disease.
We have previously shown that, unlike the general population, wherein TC and BP "track"
over time, in patients with SLE, these risk factors take a dynamic course, varying
because of changes in disease activity and treatment [5].

In this study, cumulative exposure to TC, SBP, and DBP was measured by using three
summary measures, arithmetic mean, time-adjusted mean (AM), and area-under-the-curve
(AUC). Time-dependent proportional hazards regression models were then applied to
these summary measures. In this way, a sense of cumulative exposure was captured in
two ways: first, in the form of a summary measure, and second, by determining the
hazard related to this summary measure for an interval just before each and every
sequential visit. In these time-dependent models, a sense of cumulative exposure to
other covariates, including disease activity score, corticosteroids, and antimalarials,
was also captured through the use of serial measurements of these variables, updated
from one visit to the next.

For each of TC, SBP, and DBP, mean and AM summary measures were significantly predictive
of a CAD event. In addition, the HR and accompanying P value of mean summary measures was the same as for AM summary measures in the case
of each of TC, SBP, and DBP. This is likely related to the fact that overall, in this
context, in which measurements were taken frequently, mean and AM values were very
similar for each patient. However, when applied to a setting in which measurements
are more irregular and infrequent, the AM, which is weighted for the interval between
measurements, may be expected more accurately to reflect cumulative risk exposure
and hence the overall risk of CAD. In the case of TC, a hazard ratio of 2.07 (P = 0.003) means that for every 1 mmol/L increase in mean (or AM) plasma TC level, the
hazard of a CAD outcome increases 2.07-fold. In the case of SBP, a hazard ratio of
1.025 means that for every 1-mm Hg increase in mean SBP, the hazard of CAD outcome
increases 1.025-fold.

Likewise, in the case of DBP, a hazard ratio of 1.04 means that for every 1-mm Hg
increase in mean (or AM) DBP, the hazard of CAD outcome increases 1.04-fold. SBP and
DBP are highly correlated, and, based on our analyses, either one or the other may
be used to quantify CAD risk in patients with SLE.

AUC is very closely tied to length of follow-up, and for any given variable may only
become larger over time. As such, it does not provide a sense of rise and fall in
the variable of interest. Furthermore, it is not measured in the original units of
the variable from which it is derived. These reasons may underlie the lack of association
between AUC measures and CAD outcomes among SLE patients in our study.

In this proof-of-concept study, our chosen lipid marker of CAD risk was TC. In general,
low-density lipoprotein cholesterol (LDL-C) is deemed the primary target of lipid-lowering
therapy [11]. In the Toronto lupus cohort, measurement of nonfasting TC has been routine practice
at every visit since 1975. However, measurement of lipid and lipoprotein subfractions
is a more-recent addition to the data-collection protocol and performed only once
yearly because of the need for a fasting sample. To derive summary measures and test
their ability to quantify CAD risk, we required a very large dataset inclusive of
a relatively large number of CAD outcomes. The TC and BP datasets fulfilled these
methodologic requirements. In future, summary measures derived in this study may be
applied to other risk factors, such as LDL-C.

How many measurements are enough to provide a valid summary measure, and how often
should these measurements be taken in patients with SLE? In this study, the average
of first two TC (or SBP or DBP) measurements was not significantly predictive of CAD
outcome. Therefore, ideally three or more serial measurements should be sought. Although
in this study, the mean gap between measurements used to calculate summary measures
was 4.3 months, patients in whom the gap between one or more serial measurements exceeded
18 months were not included in the analyses. Further studies are required to determine
the optimal number and frequency of measurements of TC and BP in evaluating CAD risk
in SLE.

This study has provided several important insights into the role of demographic-,
disease-, and treatment-related variables in SLE-related CAD. The most noteworthy
are the increased risk of CAD with increasing disease activity and corticosteroid
use, and the protective effect of antimalarials.

We found that for every unit increase in recent SLEDAI-2K disease activity score,
the risk of CAD event by the time of the subsequent visit increased almost 10%. An
increase in SLEDAI-2K score of 4 or more is generally deemed clinically significant
[12]. This means that a minimum clinically significant increase in recent disease activity
score is associated with approximately 46% increase in risk of CAD in the interval
between sequential visits. Ibanez et al. [13] previously showed that for every unit increase in the time-adjusted mean SLEDAI-2K score (AMS), the hazard of a CAD outcome increases 1.08-fold. Collectively, these
associations highlight the underpinning role of inflammation in SLE-related CAD.

Our patients with CAD events had greater disease activity at baseline and during follow-up,
manifest in a multitude of organ systems, including musculoskeletal, cutaneous, renal,
neural, vascular, and serosal. As the overall prevalence of chronic renal impairment
was low and did not differ among groups with and without CAD, the association between
disease activity and events seen in this study cannot be attributed to nephritis or
renal impairment alone.

In studies of coronary risk factors in SLE, it is often difficult to tease apart the
effect of corticosteroids from disease activity and traditional cardiac risk factors.
In previous studies, longer duration of steroid use has been shown to be an independent
risk factor for CAD in SLE [1,14]. With a retrospective chart review method, Karp et al. [15] showed that a 10-mg increase in the average daily prednisone-equivalent dose in the
preceding year is independently associated with a 16% increase in the estimated 2-year
CAD risk. Here, we quantified the CAD risk in patients with SLE by using prospectively
collected data. Patients with CAD events received a significantly greater cumulative
dose of corticosteroids during follow-up than did those who remained CAD free. In
the regression models for first-available and average-of-first-two TC levels, exposure
to corticosteroids at any time during the course of disease, irrespective of dose
and duration of use, was associated with a dramatic 4.17-fold increased risk of a
CAD event, independent of other risk factors. This HR was reduced to 1.85, but remained
substantial and statistically significant, in the time-dependent models wherein recent
exposure to corticosteroids was related to a CAD event.

In time-constant models for TC and BP, antimalarial use during the course of SLE was
associated with a remarkable 50% to 59% reduction in hazard of a CAD event. The lack
of a significant association between recent antimalarial use and CAD in the time-dependent
models may point to a beneficial effect with long-term rather than short-term use.
In previous studies, use of antimalarials has been associated with a reduction in
TC level [5,14,16,17]. However, ours is one of only a few studies in which the use of antimalarials has
been linked with a reduction in the risk of actual CAD events [18,19]. Furthermore, the halving of coronary risk makes a strong case for the use of antimalarials
in patients with SLE, not only to control disease activity but also for cardioprotection.

As in previous studies, we have shown that male sex and older age are associated with
increased hazard of a CAD event. In previous studies, anti-phospholipid antibodies,
diabetes, and smoking were shown to be independent risk factors for CAD events in
SLE [20]. However, in this study, in multiple regression analysis, such an association was
not found, possibly because of the limited number of patients who smoke or have diabetes
or anti-phospholipid antibodies. This study is not an exhaustive evaluation of traditional
risk factors, and the role of other variables, such as family history of ischemic
heart disease, body mass index (BMI), and waist/hip ratio, merit further investigation
in future studies.

In summary, we showed that elevated TC and BP are both potentially treatable risk
factors for CAD in SLE. Our study highlighted the importance of frequent measurements
of BP and TC in management of patients with SLE. Although clinicians might consider
the need for change in treatment based on single TC and BP measurements, their decision
actually to do so should be made on the basis of the mean of several measurements.

Conclusions

Overall, this study has both conceptual and practical significance. From a conceptual
point of view, our findings illustrate that in a systemic inflammatory disease, for
a dynamic risk factor such as TC or BP, summary measures such as mean and AM better
reflect cumulative exposure and hence better quantify CAD risk. This is an important
consideration in future studies of dynamic risk factors for CAD in a chronic relapsing-remitting
disease such as SLE and may be used to derive risk-prediction models specifically
for SLE. From a practical point of view, this study has shown that assessment of coronary
risk related to TC and BP in patients with SLE relies on serial measurement of these
risk factors throughout the relapsing-remitting course of SLE. Additionally, this
study has shown that disease activity and corticosteroid use are CAD risk factors
in SLE, indicating that disease activity should be recognized and optimally controlled
by using the minimal effective dose of corticosteroids. The demonstration of a cardioprotective
association of antimalarials highlights the staple role of this class of drugs in
the management of patients with SLE. Finally, this study has quantified the risk associated
with exposure to TC, SBP, and DBP over time, emphasizing the importance of these potentially
treatable traditional risk factors in patients with SLE. Future efforts must be directed
toward determining TC and BP cut-points for CAD risk stratification specifically among
patients with SLE and the role of treatment of risk factors in reducing the incidence
of CAD events.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

MN participated in the study design, collection and analysis of data, interpretation
of results, and preparation of the manuscript. DDG and MBU participated in the study
design, collection of data, interpretation of results, and preparation of the manuscript.
DI participated in the study design, analysis of data, interpretation of results,
and preparation of the manuscript. PJH participated in the study design, interpretation
of results, and preparation of the manuscript. All authors read and approved the manuscript
for publication.

Acknowledgements

This study was supported by the Centre for Prognosis Studies in The Rheumatic Diseases,
The Smythe Foundation, Lupus Flare Foundation, Ontario Lupus Association, and The
Lupus Society of Alberta. Dr. Nikpour was supported by the Arthritis Centre of Excellence
and the Geoff Carr Lupus Fellowship.